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A generalizable deep learning framework for localizing and characterizing acoustic emission sources in riveted metallic panels
یک چارچوب یادگیری عمیق قابل تعمیم برای محلی سازی و توصیف منابع انتشار صوتی در پانل های فلزی پرچین-2019 This paper introduces a deep learning-based framework to localize and characterize acoustic
emission (AE) sources in plate-like structures that have complex geometric features,
such as doublers and rivet connections. Specifically, stacked autoencoders are
pre-trained and utilized in a two-step approach that first localizes AE sources and then
characterizes them. To achieve these tasks with only one AE sensor, the paper leverages
the reverberation patterns, multimodal characteristics, and dispersive behavior of AE
waveforms. The considered waveforms include AE sources near rivet connections, on the
surface of the plate-like structure, and on its edges. After identifying AE sources that occur
near rivet connections, the proposed framework classifies them into four source-to-rivet
distance categories. In addition, the paper investigates the sensitivity of localization results
to the number of sensors and compares their localization accuracy with the triangulation
method as well as machine learning algorithms, including support vector machine (SVM)
and shallow neural network. Moreover, the generalization of the deep learning approach
is evaluated for typical scenarios in which the training and testing conditions are not identical.
To train and test the performance of the proposed approach, Hsu-Nielsen pencil lead
break tests were carried out on two identical aluminum panels with a riveted stiffener. The
results demonstrate the effectiveness of the deep learning-based framework for singlesensor,
AE-based structural health monitoring of plate-like structures. Keywords: Acoustic emission | Deep learning | Edge reflection | Reverberation patterns | Plate-like structures | Pattern recognition | Stacked autoencoders | Guided ultrasonic waves | Machine learning | Structural health monitoring |
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